Nomogram for Prediction of Lymph Node Metastasis in Patients with Superficial Esophageal Squamous Cell Carcinoma.

JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY(2020)

引用 28|浏览35
暂无评分
摘要
Background and Aim Knowledge of lymph node metastasis (LNM) status is crucial to determine whether patients with superficial esophageal squamous cell carcinoma (ESCC) can be cured with endoscopic resection alone, without the need for additional esophagectomy. The present study aimed to identify predictive factors and develop a prediction model for LNM in patients with superficial ESCC. Methods Clinicopathologic data from 501 patients with superficial ESCC treated with radical esophagectomy were reviewed. Stepwise logistic regression analysis determined the predictors of LNM. Using these predictors, a nomogram for predicting the risk of LNM was constructed and internally validated using a bootstrap resampling method. Results LNM rates of tumors invading the lamina propria, muscularis mucosa, and SM1 layers were 3.7%, 15.5%, and 40.7%, respectively. Deep tumor invasion depth, moderately or poorly differentiated histology, and lymphovascular invasion were independent predictors of LNM. ESCC with muscularis mucosa and SM1 invasion had odds ratios of 3.635 and 11.834, respectively, compared with that for ESCC confined to the lamina propria. Large tumor size (>2.0 cm) and presence of tumor budding showed borderline significance for LNM prediction. These five variables were incorporated into a nomogram. A constructed nomogram showed good calibration and good discrimination with an area under the receiver-operating characteristic curve (area under the curve [AUC]) of 0.812. After bootstrapping, AUC was 0.811. Conclusions We developed a nomogram that can facilitate individualized prediction of risk of LNM in patients with superficial ESCC. This model can aid in decision-making for the need for additional esophagectomy after endoscopic resection for superficial ESCC.
更多
查看译文
关键词
esophageal squamous cell carcinoma,esophagectomy,lymph node metastasis
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要